Building High Performing Schools: A Case Study
Bibliographic record
Abstract
It has long been recognized that education plays a crucial role in the development of a nation and its people. Therefore, it is logical that there is much interest in increasing the quality of the education system. Schools are a significant representative of this system, and in recent years, there has been growing attention to the concept of the high-performance school. These schools play a special role in cultivating an environment with the best teachers that challenge and develop students both academically and socially, promoting excellence. This article specifically focuses on the question of how the internal organization and management of a school can be elevated to the level of high-performance using a quality improvement framework. After identifying from the literature a potentially suitable framework for the transition of a school to the high-performance level, we applied this framework to a case school using a questionnaire and semi-structured interviews to collect data. Finding is that the selected high-performance framework can indeed help schools evaluate their current status relative to the high-performance level and guide them in the right direction towards excellence, by providing practical points for improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".